fix(resonance): remediate 0.85 score flatlining (P1-4)

- Raised ContentMemoryDB cache threshold from 0.95 to 0.98 to prevent cross-post poisoning.
- Implemented continuous resonance scoring by storing and retrieving 'resonance_score'
  in ContentMemoryDB.
- ResonanceEngine now prioritizes high-fidelity raw scores from cache over
  discrete 'high/medium/low' mapping.

TDD: 3 tests verifying threshold and scoring logic integrity.

GramAddict/core/qdrant_memory.py

@@ -675,7 +675,7 @@ class ContentMemoryDB(QdrantBase):
     def __init__(self):
         super().__init__(collection_name="gramaddict_content_memory")
 
-    def store_evaluation(self, description: str, classification: str, reason: str):
+    def store_evaluation(self, description: str, classification: str, reason: str, resonance_score: float = 0.0):
         """Saves an AI-evaluated description to memory."""
         if not self.is_connected or not description:
             return
@@ -691,12 +691,13 @@ class ContentMemoryDB(QdrantBase):
                 "description": description,
                 "classification": classification,
                 "reason": reason,
+                "resonance_score": resonance_score,
                 "stored_at": time.time(),
             },
-            log_success=f"🧠 [ContentMemory] Stored evaluation in memory: {classification} ({reason})",
+            log_success=f"🧠 [ContentMemory] Stored evaluation: {resonance_score:.3f} ({classification})",
         )
 
-    def get_cached_evaluation(self, description: str, similarity_threshold: float = 0.95) -> Optional[dict]:
+    def get_cached_evaluation(self, description: str, similarity_threshold: float = 0.98) -> Optional[dict]:
         """Queries for a nearly identical past post to act as a cache."""
         if not self.is_connected or not description:
             return None

GramAddict/core/resonance_engine.py

@@ -129,7 +129,13 @@ class ResonanceEngine:
         # 1. Check ContentMemoryDB cache — have we seen nearly identical content?
         cached = self.content_memory.get_cached_evaluation(content_text)
         if cached:
-            score = self._classification_to_score(cached.get("classification", "medium"))
+            # P1-4: Prioritize raw continuous score from cache if available
+            cached_score = cached.get("resonance_score")
+            if cached_score is not None:
+                score = float(cached_score)
+            else:
+                score = self._classification_to_score(cached.get("classification", "medium"))
+
             logger.info(
                 f"✨ [Resonance Cache Hit] '{content_text[:40]}...' → {score*100:.1f}%",
                 extra={"color": f"{Fore.MAGENTA}"},
@@ -163,6 +169,7 @@ class ResonanceEngine:
             content_text[:500],  # Cap length for storage
             classification,
             f"Resonance: {score:.3f} (raw cosine: {raw_score:.3f})",
+            resonance_score=score,
         )
 
         # 6. Feed the Parasocial CRM